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from modules.Device import Device
import torch
def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False):
"""#### Cast a weight tensor to a specified dtype and device.
#### Args:
- `weight` (torch.Tensor): The weight tensor.
- `dtype` (torch.dtype): The data type.
- `device` (torch.device): The device.
- `non_blocking` (bool): Whether to use non-blocking transfer.
- `copy` (bool): Whether to copy the tensor.
#### Returns:
- `torch.Tensor`: The casted weight tensor.
"""
if device is None or weight.device == device:
if not copy:
if dtype is None or weight.dtype == dtype:
return weight
return weight.to(dtype=dtype, copy=copy)
r = torch.empty_like(weight, dtype=dtype, device=device)
r.copy_(weight, non_blocking=non_blocking)
return r
def cast_to_input(weight, input, non_blocking=False, copy=True):
"""#### Cast a weight tensor to match the input tensor.
#### Args:
- `weight` (torch.Tensor): The weight tensor.
- `input` (torch.Tensor): The input tensor.
- `non_blocking` (bool): Whether to use non-blocking transfer.
- `copy` (bool): Whether to copy the tensor.
#### Returns:
- `torch.Tensor`: The casted weight tensor.
"""
return cast_to(
weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy
)
def cast_bias_weight(s: torch.nn.Module, input: torch.Tensor= None, dtype:torch.dtype = None, device:torch.device = None, bias_dtype:torch.dtype = None) -> tuple:
"""#### Cast the bias and weight of a module to match the input tensor.
#### Args:
- `s` (torch.nn.Module): The module.
- `input` (torch.Tensor): The input tensor.
#### Returns:
- `tuple`: The cast weight and bias.
"""
if input is not None:
if dtype is None:
dtype = input.dtype
if bias_dtype is None:
bias_dtype = dtype
if device is None:
device = input.device
bias = None
non_blocking = Device.device_supports_non_blocking(device)
if s.bias is not None:
has_function = s.bias_function is not None
bias = cast_to(
s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function
)
if has_function:
bias = s.bias_function(bias)
has_function = s.weight_function is not None
weight = cast_to(
s.weight, dtype, device, non_blocking=non_blocking, copy=has_function
)
if has_function:
weight = s.weight_function(weight)
return weight, bias
class CastWeightBiasOp:
"""#### Class representing a cast weight and bias operation."""
comfy_cast_weights: bool = False
weight_function: callable = None
bias_function: callable = None
class disable_weight_init:
"""#### Class representing a module with disabled weight initialization."""
class Linear(torch.nn.Linear, CastWeightBiasOp):
"""#### Linear layer with disabled weight initialization."""
def reset_parameters(self):
"""#### Reset the parameters of the Linear layer."""
return None
def forward_comfy_cast_weights(self, input):
"""#### Forward pass with comfy cast weights.
#### Args:
- `input` (torch.Tensor): The input tensor.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
weight, bias = cast_bias_weight(self, input)
return torch.nn.functional.linear(input, weight, bias)
def forward(self, *args, **kwargs):
"""#### Forward pass for the Linear layer.
#### Args:
- `*args`: Variable length argument list.
- `**kwargs`: Arbitrary keyword arguments.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
"""#### Conv1d layer with disabled weight initialization."""
def reset_parameters(self):
"""#### Reset the parameters of the Conv1d layer."""
return None
def forward_comfy_cast_weights(self, input):
"""#### Forward pass with comfy cast weights.
#### Args:
- `input` (torch.Tensor): The input tensor.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
weight, bias = cast_bias_weight(self, input)
return self._conv_forward(input, weight, bias)
def forward(self, *args, **kwargs):
"""#### Forward pass for the Conv1d layer.
#### Args:
- `*args`: Variable length argument list.
- `**kwargs`: Arbitrary keyword arguments.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
"""#### Conv2d layer with disabled weight initialization."""
def reset_parameters(self) -> None:
"""#### Reset the parameters of the Conv2d layer."""
return None
def forward_cast_weights(self, input: torch.Tensor) -> torch.Tensor:
"""#### Forward pass with comfy cast weights.
#### Args:
- `input` (torch.Tensor): The input tensor.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
weight, bias = cast_bias_weight(self, input)
return self._conv_forward(input, weight, bias)
def forward(self, *args, **kwargs) -> torch.Tensor:
"""#### Forward pass for the Conv2d layer.
#### Args:
- `*args`: Variable length argument list.
- `**kwargs`: Arbitrary keyword arguments.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
if self.comfy_cast_weights:
return self.forward_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class Conv3d(torch.nn.Conv3d, CastWeightBiasOp):
"""#### Conv3d layer with disabled weight initialization."""
def reset_parameters(self):
"""#### Reset the parameters of the Conv3d layer."""
return None
def forward_comfy_cast_weights(self, input):
"""#### Forward pass with comfy cast weights.
#### Args:
- `input` (torch.Tensor): The input tensor.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
weight, bias = cast_bias_weight(self, input)
return self._conv_forward(input, weight, bias)
def forward(self, *args, **kwargs):
"""#### Forward pass for the Conv3d layer.
#### Args:
- `*args`: Variable length argument list.
- `**kwargs`: Arbitrary keyword arguments.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp):
"""#### GroupNorm layer with disabled weight initialization."""
def reset_parameters(self) -> None:
"""#### Reset the parameters of the GroupNorm layer."""
return None
def forward_comfy_cast_weights(self, input):
"""#### Forward pass with comfy cast weights.
#### Args:
- `input` (torch.Tensor): The input tensor.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
weight, bias = cast_bias_weight(self, input)
return torch.nn.functional.group_norm(
input, self.num_groups, weight, bias, self.eps
)
def forward(self, *args, **kwargs):
"""#### Forward pass for the GroupNorm layer.
#### Args:
- `*args`: Variable length argument list.
- `**kwargs`: Arbitrary keyword arguments.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
"""#### LayerNorm layer with disabled weight initialization."""
def reset_parameters(self) -> None:
"""#### Reset the parameters of the LayerNorm layer."""
return None
def forward_cast_weights(self, input: torch.Tensor) -> torch.Tensor:
"""#### Forward pass with cast weights.
#### Args:
- `input` (torch.Tensor): The input tensor.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
if self.weight is not None:
weight, bias = cast_bias_weight(self, input)
else:
weight = None
bias = None
return torch.nn.functional.layer_norm(
input, self.normalized_shape, weight, bias, self.eps
)
def forward(self, *args, **kwargs) -> torch.Tensor:
"""#### Forward pass for the LayerNorm layer.
#### Args:
- `*args`: Variable length argument list.
- `**kwargs`: Arbitrary keyword arguments.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
if self.comfy_cast_weights:
return self.forward_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp):
"""#### ConvTranspose2d layer with disabled weight initialization."""
def reset_parameters(self):
"""#### Reset the parameters of the ConvTranspose2d layer."""
return None
def forward_comfy_cast_weights(self, input, output_size=None):
"""#### Forward pass with comfy cast weights.
#### Args:
- `input` (torch.Tensor): The input tensor.
- `output_size` (torch.Size): The output size.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
num_spatial_dims = 2
output_padding = self._output_padding(
input,
output_size,
self.stride,
self.padding,
self.kernel_size,
num_spatial_dims,
self.dilation,
)
weight, bias = cast_bias_weight(self, input)
return torch.nn.functional.conv_transpose2d(
input,
weight,
bias,
self.stride,
self.padding,
output_padding,
self.groups,
self.dilation,
)
def forward(self, *args, **kwargs):
"""#### Forward pass for the ConvTranspose2d layer.
#### Args:
- `*args`: Variable length argument list.
- `**kwargs`: Arbitrary keyword arguments.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
"""#### ConvTranspose1d layer with disabled weight initialization."""
def reset_parameters(self):
"""#### Reset the parameters of the ConvTranspose1d layer."""
return None
def forward_comfy_cast_weights(self, input, output_size=None):
"""#### Forward pass with comfy cast weights.
#### Args:
- `input` (torch.Tensor): The input tensor.
- `output_size` (torch.Size): The output size.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
num_spatial_dims = 1
output_padding = self._output_padding(
input,
output_size,
self.stride,
self.padding,
self.kernel_size,
num_spatial_dims,
self.dilation,
)
weight, bias = cast_bias_weight(self, input)
return torch.nn.functional.conv_transpose1d(
input,
weight,
bias,
self.stride,
self.padding,
output_padding,
self.groups,
self.dilation,
)
def forward(self, *args, **kwargs):
"""#### Forward pass for the ConvTranspose1d layer.
#### Args:
- `*args`: Variable length argument list.
- `**kwargs`: Arbitrary keyword arguments.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
return super().forward(*args, **kwargs)
class Embedding(torch.nn.Embedding, CastWeightBiasOp):
"""#### Embedding layer with disabled weight initialization."""
def reset_parameters(self):
"""#### Reset the parameters of the Embedding layer."""
self.bias = None
return None
def forward_comfy_cast_weights(self, input, out_dtype=None):
"""#### Forward pass with comfy cast weights.
#### Args:
- `input` (torch.Tensor): The input tensor.
- `out_dtype` (torch.dtype): The output data type.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
output_dtype = out_dtype
if (
self.weight.dtype == torch.float16
or self.weight.dtype == torch.bfloat16
):
out_dtype = None
weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
return torch.nn.functional.embedding(
input,
weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
).to(dtype=output_dtype)
def forward(self, *args, **kwargs):
"""#### Forward pass for the Embedding layer.
#### Args:
- `*args`: Variable length argument list.
- `**kwargs`: Arbitrary keyword arguments.
#### Returns:
- `torch.Tensor`: The output tensor.
"""
if self.comfy_cast_weights:
return self.forward_comfy_cast_weights(*args, **kwargs)
else:
if "out_dtype" in kwargs:
kwargs.pop("out_dtype")
return super().forward(*args, **kwargs)
@classmethod
def conv_nd(s, dims: int, *args, **kwargs) -> torch.nn.Conv2d:
"""#### Create a Conv2d layer with the specified dimensions.
#### Args:
- `dims` (int): The number of dimensions.
- `*args`: Variable length argument list.
- `**kwargs`: Arbitrary keyword arguments.
#### Returns:
- `torch.nn.Conv2d`: The Conv2d layer.
"""
if dims == 2:
return s.Conv2d(*args, **kwargs)
elif dims == 3:
return s.Conv3d(*args, **kwargs)
else:
raise ValueError(f"unsupported dimensions: {dims}")
class manual_cast(disable_weight_init):
"""#### Class representing a module with manual casting."""
class Linear(disable_weight_init.Linear):
"""#### Linear layer with manual casting."""
comfy_cast_weights: bool = True
class Conv1d(disable_weight_init.Conv1d):
"""#### Conv1d layer with manual casting."""
comfy_cast_weights = True
class Conv2d(disable_weight_init.Conv2d):
"""#### Conv2d layer with manual casting."""
comfy_cast_weights: bool = True
class Conv3d(disable_weight_init.Conv3d):
"""#### Conv3d layer with manual casting."""
comfy_cast_weights = True
class GroupNorm(disable_weight_init.GroupNorm):
"""#### GroupNorm layer with manual casting."""
comfy_cast_weights: bool = True
class LayerNorm(disable_weight_init.LayerNorm):
"""#### LayerNorm layer with manual casting."""
comfy_cast_weights: bool = True
class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
"""#### ConvTranspose2d layer with manual casting."""
comfy_cast_weights = True
class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
"""#### ConvTranspose1d layer with manual casting."""
comfy_cast_weights = True
class Embedding(disable_weight_init.Embedding):
"""#### Embedding layer with manual casting."""
comfy_cast_weights = True
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